{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "23cf319b",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/WeaviateIndex_metadata_filter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
   "metadata": {},
   "source": [
    "# Weaviate Vector Store Metadata Filter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5508d8ac",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0beb6603",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-vector-stores-weaviate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b05d5956",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index weaviate-client"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
   "metadata": {},
   "source": [
    "#### Creating a Weaviate Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ad68ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"sk-<your key here>\"\n",
    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eccceb71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df8b27e5-5ad5-4dfe-90c7-0cf1f1d1b37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import weaviate\n",
    "\n",
    "# cloud\n",
    "resource_owner_config = weaviate.AuthClientPassword(\n",
    "    username=\"\",\n",
    "    password=\"\",\n",
    ")\n",
    "client = weaviate.Client(\n",
    "    \"https://test.weaviate.network\",\n",
    "    auth_client_secret=resource_owner_config,\n",
    ")\n",
    "\n",
    "# local\n",
    "# client = weaviate.Client(\"http://localhost:8081\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
   "metadata": {},
   "source": [
    "#### Load documents, build the VectorStoreIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a2bcc07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import VectorStoreIndex\n",
    "from llama_index.vector_stores.weaviate import WeaviateVectorStore\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3a41a70d",
   "metadata": {},
   "source": [
    "## Metadata Filtering\n",
    "\n",
    "Let's insert a dummy document, and try to filter so that only that document is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df6b6d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.schema import TextNode\n",
    "\n",
    "nodes = [\n",
    "    TextNode(\n",
    "        text=\"The Shawshank Redemption\",\n",
    "        metadata={\n",
    "            \"author\": \"Stephen King\",\n",
    "            \"theme\": \"Friendship\",\n",
    "            \"year\": 1994,\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"The Godfather\",\n",
    "        metadata={\n",
    "            \"director\": \"Francis Ford Coppola\",\n",
    "            \"theme\": \"Mafia\",\n",
    "            \"year\": 1972,\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"Inception\",\n",
    "        metadata={\n",
    "            \"director\": \"Christopher Nolan\",\n",
    "            \"theme\": \"Fiction\",\n",
    "            \"year\": 2010,\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"To Kill a Mockingbird\",\n",
    "        metadata={\n",
    "            \"author\": \"Harper Lee\",\n",
    "            \"theme\": \"Mafia\",\n",
    "            \"year\": 1960,\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"1984\",\n",
    "        metadata={\n",
    "            \"author\": \"George Orwell\",\n",
    "            \"theme\": \"Totalitarianism\",\n",
    "            \"year\": 1949,\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"The Great Gatsby\",\n",
    "        metadata={\n",
    "            \"author\": \"F. Scott Fitzgerald\",\n",
    "            \"theme\": \"The American Dream\",\n",
    "            \"year\": 1925,\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"Harry Potter and the Sorcerer's Stone\",\n",
    "        metadata={\n",
    "            \"author\": \"J.K. Rowling\",\n",
    "            \"theme\": \"Fiction\",\n",
    "            \"year\": 1997,\n",
    "        },\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30b0b2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core import StorageContext\n",
    "\n",
    "vector_store = WeaviateVectorStore(\n",
    "    weaviate_client=client, index_name=\"LlamaIndex_filter\"\n",
    ")\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "index = VectorStoreIndex(nodes, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfe83ebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "data": {
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0.033302948, -0.023625715, 0.03212995, -0.01994097, -0.02603546, 0.0052306796, 0.020807968, -0.0012869511, -0.0049916175, -0.035597946], metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='110b4ab08da17685bdc3d53aecf6085a535dd00a43612eed991bce8074aa36a9', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.83969581)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever = index.as_retriever()\n",
    "retriever.retrieve(\"What is inception?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1bd18f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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-0.003677491, 0.013412767, -0.006926781, 0.011460674, 0.008878874, 0.007701321, 0.027127793, -0.04339943, 0.022757625, -0.02745524, -0.007354982, 0.038361773, 0.013337202, -0.0053304727, -0.016259044, -0.02550315], metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia', 'year': 1972}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='79563896e320da86be371351f55d903acdcfb3229368a6622f6be6e929e8b7cc', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.7620960999999999)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from llama_index.core.vector_stores import (\n",
    "    MetadataFilter,\n",
    "    MetadataFilters,\n",
    "    FilterOperator,\n",
    ")\n",
    "\n",
    "\n",
    "filters = MetadataFilters(\n",
    "    filters=[\n",
    "        MetadataFilter(key=\"theme\", operator=FilterOperator.EQ, value=\"Mafia\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "retriever = index.as_retriever(filters=filters)\n",
    "retriever.retrieve(\"What is inception about?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "642bef1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "data": {
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-0.003677491, 0.013412767, -0.006926781, 0.011460674, 0.008878874, 0.007701321, 0.027127793, -0.04339943, 0.022757625, -0.02745524, -0.007354982, 0.038361773, 0.013337202, -0.0053304727, -0.016259044, -0.02550315], metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia', 'year': 1972}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='79563896e320da86be371351f55d903acdcfb3229368a6622f6be6e929e8b7cc', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.74872893)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n",
    "\n",
    "\n",
    "filters = MetadataFilters(\n",
    "    filters=[\n",
    "        MetadataFilter(key=\"theme\", value=\"Mafia\"),\n",
    "        MetadataFilter(key=\"year\", value=1972),\n",
    "    ]\n",
    ")\n",
    "\n",
    "retriever = index.as_retriever(filters=filters)\n",
    "retriever.retrieve(\"What is inception?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ca00ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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-0.02214766, 0.004932771, -0.0010275267, 0.006934801, -0.020296665, -0.00048202972, -0.027404997], metadata={'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='a2656c2bc96ed472bb0ed3ea81075042e9860987f3156428789d07079e019ed0', text=\"Harry Potter and the Sorcerer's Stone\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.8498219300000001)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from llama_index.core.vector_stores import FilterOperator, FilterCondition\n",
    "\n",
    "\n",
    "filters = MetadataFilters(\n",
    "    filters=[\n",
    "        MetadataFilter(key=\"theme\", value=\"Fiction\"),\n",
    "        MetadataFilter(key=\"year\", value=1997, operator=FilterOperator.GT),\n",
    "    ],\n",
    "    condition=FilterCondition.OR,\n",
    ")\n",
    "\n",
    "retriever = index.as_retriever(filters=filters)\n",
    "retriever.retrieve(\"Harry Potter?\")"
   ]
  }
 ],
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